Quality control for quantitative PCR based on amplification - PowerPoint PPT Presentation

Quality control for quantitative PCR based on amplification compatibility test ������������� �������������������������

Error stratification throughout preanalytics single tissue liver blood cell culture cell gene ACTB IL1B CASP3 FGF7 ACTB IL1B CASP3 IFNG ACTB H3 IL8 BCL2 18S Mean Cq 20.41 26.76 27.25 31.52 16.05 17.6 24.71 32.2 15.87 20.1 23.4 28.5 29.95 I.S. var. Subject 0.00 0.00 0.00 0.00 0.07 0.94 0.00 0.95 0.00 0.00 0.00 0.00 0.00 Processing SD (cycles) Sampling 1.56 1.64 1.20 0.40 0.10 0.00 0.11 0.00 0.37 0.20 0.29 0.20 1.90 noise RT 0.46 0.39 0.27 0.90 0.21 0.32 0.18 0.24 0.35 0.35 0.31 0.21 0.30 qPCR 0.07 0.12 0.08 0.39 0.18 0.20 0.13 0.40 0.21 0.10 0.09 0.16 0.51 Total noise 1.63 1.70 1.23 1.06 0.31 1.01 0.25 1.06 0.55 0.42 0.44 0.33 1.99 ∼ 1.5 ∼ 0.66 ∼ 0.44 ∼ 2

Liver tissue

Blood samples

Cell culture

Single cell

Noise contribution by various sample processing steps

Noise contribution by various sample processing steps

Real-time PCR response curve - Cq values

If a biological sample is inhibited, technical replicates WILL NOT protect us from the error Any difference in the Cq between different samples may be due to a biological effect or due to INHIBITION!!! Therefore, Cq is not suitable as a quality control measure Kinetics of the reactions is much more reliable Because kinetics must be compatible among samples, regardless of the initial DNA concentration

Amplification kinetics example of incompatible samples

The multivariate distance from the centre of the reference set

Visual check may sometimes be impossible The kinetics must be digitalised and the obtained parameters compared statistically.

Discrepancy between methods for amplification efficiency estimation from single sample ��������������������������� ��������������!�"� Tichopad et al. Ramakers et Peirson et al. Wilhelm et al. Liu & Saint al. � E SD � E SD � E SD � E SD � E SD 0.44 0.076 0.26 0.102 0.24 0.118 0.31 0.076 0.33 0.071 E std =10 -1/slope -1 � E= E std - Ē individual

Kinetics parameters for amplification outlier detection 38 fitted curve Maximum of the first signal readings 34 [FD_max] and second first derivative 30 derivative [SD_max] are second derivative used to identify 26 amplification kinetics in 22 2D space 18 14 10 6 FD_max 2 SD_max -2 -5 5 15 25 35 45 x''max PI

Multivariate outlier detection To disclose defective samples Test samples ● vs. reference set ▲ . Flagged points were excluded from the reference set. The inner lines define traditional univariate boundaries for outliers obtained as upper quartile plus 1.5 times interquartile range and lower quartile minus 1.5 times interquartile range.

Validation experiments EXPERIMENT 1: One assay varying inhibition strength 3 x 5 serial dilutions were produced as non-inhibited reference set (n=15) and inhibited sets (each n=15) with 1%, 2%, 4%, 8%, and 16% of primer competamers added to regular primer concentration. Primer competimers were used to introduce the inhibition. EXPERIMENT 2: Three assays constant inhibition strength Three assays as standard curves were performed. Each standard curve consisted of 5-fold dilutions (1-, 5-, 25-, 125-, and 625-fold) in triplicates (total 15 reactions). Two standard curves were produced from the same cDNA stock solution, one without inhibitor and one with 2.0 ng tannic acid added per 15 µl reaction mix.

EXPERIMENT 1 Effect of the inhibition by primer competimers on the Cq value

EXPERIMENT 1 Effect of the inhibition by primer competimers on the Cq value Differences from reference [ � Cq] for various inhibition strengths DNA conc. 1% 2% 4% 8% 16% x*10000 (n=3) 0.05 -0.29 -0.223 -0.257 -0.363 x*1000 (n=3) 0.233 0.327 0.143 -0.077 -0.307 x*100 (n=3) 0.213 0.017 -0.073 -0.303 -0.47 x*10 (n=3) -0.23 -0.173 -0.457 -0.753 -0.737 x (n=3) NA NA NA NA NA p of t-test (H0: 0.58 0.84 0.31 0.09 0.02 Dif<0) NA – too large scatter of the reference to reliably calculate the � Cq.

EXPERIMENT 1 Retrieval of samples inhibited by primer competimers by the multivariate vs. univariate test Multivariate (Z) 1% 2% 4% 8% 16% NTC N/Total 6/15 2/15 2/15 11/15 15/15 6/6 Retrieval [%] 40% 13% 13% 73% 100% 100% Univariate (E) N/Total 4/15 5/15 2/15 1/15 2/15 2/6 Retrieval [%] 27% 33% 13% 7% 13% 13% Bar et al (2003) Bar T, Stahlberg A, Muszta A, Kubista M. (2003). Nucleic Acids Res. 31, e105

EXPERIMENT 1 Retrieval of samples inhibited by primer competimers by the multivariate vs. univariate test Bar T, Stahlberg A, Muszta A, Kubista M. (2003). Nucleic Acids Res. 31, e105

One parameter vs. two parameters in detecting kinetics outliers Multivariate Mahalanobis DISTANCE calculated Maximum of the first derivative (FDM) from the maximum of the first derivative (FDM) and the maximum of the second derivative (SDM)

EXPERIMENT 2 Retrieval of samples inhibited by tannic acid by the multivariate vs. univariate test ACTB H3 IGF Multivariate (Z) 12/15 15/15 10/15 N/Total Retrieval [%] 80% 100% 67% Univariate (E) N/Total 1/15 5/15 2/15 Retrieval [%] 7% 33% 13% Bar T, Stahlberg A, Muszta A, Kubista M. (2003). Nucleic Acids Res. 31, e105

Multivariate KOD using Kineret software ���#��������#���

Calculation ���!���$��������"����������% � &###&% � �����������������&� �����'������(��������(��������!����)*�����������������!�����+ 2 ... 2 = + + Z X X 1 n ������)���������'������(� χ ������*�,!������������!����������� ��$������ � ������ -������+�./0��'�����1/0��'��������������� -������ -������ -������ 1/0��'2./0��'�3���4���4� τ 5������������������������������� ������������ τ ������������!��� ������ ��������������������$������������# τ 21/0��'�� 1/0��' 2 ... 2 _ max = SD + + τ Z ���� �����)*�����+ ���������"��!��� �������6�7�����667����������(�� ����� χ �� �������!����� ������ ������#66������6#���&���������"��(#

Use of Kineret within SPIDIA Objective : amplification compatibility as an additional RNA quality Kineret Version 1.0.5 was used The reference set: samples REFpool RNA + REF RNA from all the participating laboratories. The Z score (called Kinetics Distance – KD) of the three qPCR technical replicates of each sample were averaged. The test set: RNA from samples A and B were compared with the reference set KDs of each group are presented in box-whisker plots

KD distribution as calculated by the Kineret software of the four gene transcripts in each sample Gene Sample N min median max IQR Sample A 10 0.0 2.0 16.9 3.2 FOS Sample B 9 0.1 4.3 96.1 14.5 REF 8 0.0 0.8 4.1 1.8 Sample A 9 0.0 2.4 117.5 6.4 GAPDH Sample B 9 0.3 2.9 30.0 4.4 REF 8 0.1 1.2 3.9 2.2 Sample A 10 0.2 3.2 33.8 6.7 IL1B Sample B 9 0.1 3.7 55.1 8.6 REF 8 0.2 0.7 16.9 2.1 Sample A 10 0.1 2.6 14.3 3.8 IL8 Sample B 9 0.1 3.2 48.8 8.2 REF 8 0.2 2.1 5.1 1.7

Kinetics Distance Kinetics Distance Kinetics Distance Kinetics Distance IL1B IL1B FOS FOS Kinetics Distance Kinetics Distance Kinetics Distance Kinetics Distance GAPDH GAPDH IL8 IL8

Conclusion Generally, multivariate methods perform better in separating defective reactions than univariate methods. Several methods can be used; e.g. the Mahalanobis distance is the uncorrected sum of squares of the principal component scores calculated from the center of the reference data set. Also other multivariate approaches may be employed such as the Kohonen self- organising networks, K-means or support vector machines.

Acknowledgements Financial grants were obtained from: • European Community seventh framework project SPIDIA (www.spidia.eu) Grant Agreement N ° : 222916 • European Community sixth framework project SmartHEALTH (www.smarthealthip.com) Grant Agreement N ° : 016817 • National R&D incubator program of Ministry of Industry and Trade of the State of Israel Original paper available in Methods 50 (2010) 308–312